ISSN 1729-3782

Vol. 5, No. 2, 146-160, 2006

AbstractSediment stability or erosion
resistance of intertidal zones depend on sediment physical characteristics and
on biological factors. Obtaining accurate data on the basic biological,
chemical and physical processes in sediments is expensive and difficult. Remote
sensing methods can produce detailed information on ecological functioning in a
cost-effective manner.

A hyperspectral image of the
Molenplaat, an intertidal flat in the Westerschelde estuary, the Netherlands,
was acquired with the HyMap sensor in June 2004. The goal of this research is
to perform, analyse and evaluate unsupervised classification methods for
sediment types on the imagery. The unsupervised methods are based on Principal
Component Analysis (PCA) or Iterative Self-Organizing Data Analysis Technique
(ISODATA), and consist of three steps: (a) classification into spectrally
distinct clusters, (b) post-clustering treatment, and (c) assignment of labels
to the clusters. The result consists of 13 clusters after the post-clustering
treatment, and of 8 or 9 classes after labelling for either the PCA or ISODATA
method. A supervised Spectral Angle Mapper (SAM) classification was performed
using field data to evaluate the unsupervised classification results. The
labelling of the unsupervised clusters was also partly based on the SAM
results, due to limited field data.

The comparison of the results reveals that 69% and 73% of the pixels of PCA and
ISODATA classification respectively were identically labelled in the supervised
classification. Moreover, the mismatches were mainly found in two classes,
while the other classes showed high similarities, indicating the plausibility
of using unsupervised classification methods for intertidal sediment types. Additional
strengths of the unsupervised classification methods are (a) the distinction of
classes that were not visited during field work and not classified in the
supervised classification, (b) the identification of spectrally distinct areas
that should be characterised during field campaigns, and (c) the
user-friendliness thanks to limited required field knowledge and short calculation
time.